Redesign Your Data Warehouse

Note: The advice in this section applies to building and optimizing data warehouses in general and is not specific to Analysis. However, since poor data warehouse design is so frequently a significant source of performance loss for Pentaho Analysis, it is listed in this section.

A data warehouse exists to consolidate and partition transactional data into one streamlined, organized, canonical source for reporting and analysis. Some guidelines to follow in data warehouse design are:

Be open to modifying the original design to meet adjusted requirements from business users (iterative design).

Remove data that is not actually used by business users.

Optimize for the right purpose. There are basically two use cases to consider: analysis (slice/dice/pivot) and static reporting. You could also use a data warehouse to cleanse and consolidate transactional data for data mining, but this model would almost certainly be inappropriate for analysis or reporting.